Duplicate pattern reconstructions

ABSTRACT

A system, method, and computer program product for multiplicating pattern images used as a trusted resource while reducing user participation, such as, for example, a producing N number (N&gt;1) of reconstructed pattern images using shared images common to at least two constructed pattern images.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims benefit of U.S. Patent Application No.62/192,525, the contents of which are hereby expressly incorporated byreference thereto in its entirety for all purposes.

FIELD OF THE INVENTION

The present invention relates generally to automated machine-implementedpattern testing, and more specifically, but not exclusively, to systems,methods, and computer program products for multiplicating patternreconstructions using a common set of images of that pattern, such as,for example, an pattern reconstruction used in pattern verificationsystems, methods, and computer program products, including fingerprintverification systems.

BACKGROUND OF THE INVENTION

The subject matter discussed in the background section should not beassumed to be prior art merely as a result of its mention in thebackground section. Similarly, a problem mentioned in the backgroundsection or associated with the subject matter of the background sectionshould not be assumed to have been previously recognized in the priorart. The subject matter in the background section merely representsdifferent approaches, which in and of themselves may also be inventions.

Pattern verification, such as in a context of registration of a trustedfingerprint into a trusted memory of a fingerprint verification system,requires a sufficiently accurate and precise image of the trustedfingerprint. This image may be obtained directly from an imaging sensor.This may be suitable for large area imaging sensors. However, for manyapplications, the trusted image is reconstituted from images of thetrusted fingerprint taken from a sensor having a sensing area smallerthan the final image. For many mobile applications and electronicplatforms, such as smartphones and the like, the use of the small areasensor provides a smaller form factor having a lower cost of goods whichis important to implementation and adoption. However use of the smallerarea sensor impacts registration and user experiences duringverification.

Any mobile electronic platform having a fingerprint verification systemtypically includes a registration system that establishes and writes oneor more trusted images into a trusted database/memory. Test fingerprintsare compared against the trusted image(s) using a matching engine thatmatches features of interest from the test fingerprint against featuresof interest from the trusted image(s). A quality of the test image andof the trusted images impact the user experience.

Whatever the size of the imaging sensor, the trusted image may bederived from a single impression from a single finger, or derived from acomposition of multiple impressions. Each solution offers differentchallenges.

It is technically possible to rely on a single impression of a singlefinger using a small area sensor for registration. However, during use,the user must not only recall which portion of which finger wasregistered, but each verification attempt relies on the user reimagingthis same portion of the same finger sufficiently close that it may bematched. As the imaging sensor area becomes smaller, reliance on asingle impression can become very frustrating for the user.

Many systems using a small area sensor therefore register a singlefinger using multiple impressions. An initial impression of an imagealways provides completely unique information of the larger image to bereconstituted. However, subsequent impressions have varying usefulnessdepending upon whether the user provides an impression that partiallyoverlaps and partially presents new image data. The user hasdifficulties in placing a finger for multiple impressions to ensure onlyunique partial overlapping data is presented with each impression,particularly over a breadth of an entire fingerprint pattern. It is tooeasy for a user to provide subsequent images with too much overlap orwith too little or no overlap. A user does not know when or if they haveprovided enough sufficiently overlapping image impressions that mapenough of the total fingerprint to provide an end product that providesthe user with an easy-to-use verification system.

Systems for processing images of a pattern may sometimes be desirablyimplemented using limited computing and memory resources. As an activearea of a sensor decreases, a greater number of images are needed tocover any particular area of the pattern. Managing and processingmultiple images can sometimes negatively impact performance.

Collection of a set of images of portions of a pattern forreconstruction of the pattern introduces a number of noise sourcesincluding the sensor, variations in the pattern itself, and aninteraction of the sensor and the pattern. This source of noise, inturn, affects a quality of the reconstructed pattern. The reconstructedpattern may be used in a number of ways and the accuracy of those usesare often influenced by the quality of the reconstructed pattern.

A user attempting to register a specific finger (the registrationrelying on a reconstruction of an image of a fingerprint from thespecific finger) multiple times would be extremely unlikely to reproduce100% exact reconstructions each time. While each reconstruction has achance, based upon a number of factors, of matching anotherreconstruction, a source of error in falsely rejecting a user because ofa failed match increases due to the inherent variations in registering areconstructed image and then testing the specific finger against thetrusted registration image.

One way to decrease a chance of a false rejection is to registermultiple reconstructed images of the same specific finger. Thesemultiple reconstructed images of the same pattern will be different fromeach other and thus more likely that one will match to a user'sverification attempt.

While the number of images in the set of images used to reconstruct animage varies, for a particular sensor area, resolution, pattern size,user overlap placement, and desired pattern coverage area, a finitenumber of images, say N number, are sampled. Conventionally, to form Mnumber of reconstructions, M×N number of images must be collected, onaverage. Collecting this number of images would be considered too timeconsuming and unwieldly in most cases. While the conventional solutionof collecting the M×N number of images likely improves performance, thesolution is unlikely to be adopted as the number of impressions to becollected from the user increases.

What is needed is a system, method, and computer program product formultiplicating pattern images used as a trusted resource while reducinguser participation.

BRIEF SUMMARY OF THE INVENTION

Disclosed is a system, method, and computer program product formultiplicating pattern images used as a trusted resource while reducinguser participation, such as, for example, a producing N number (N>1) ofreconstructed pattern images using shared images common to at least twoconstructed pattern images.

The following summary of the invention is provided to facilitate anunderstanding of some of the technical features related tomultiplicating pattern reconstructions and is not intended to be a fulldescription of the present invention. A full appreciation of the variousaspects of the invention can be gained by taking the entirespecification, claims, drawings, and abstract as a whole. The presentinvention is applicable to other pattern reconstructions in addition tofingerprint reconstructions, and to other uses in addition toreconstructed pattern registration.

In an embodiment of the present invention, a pattern source (e.g., afinger) may include a pattern (e.g., a fingerprint). For a system thatdoes not process the pattern (fingerprint) directly, the system mayprocess a representation (e.g., a pattern map) of the pattern(fingerprint). When a user uses an impressioner (e.g., places a patternsource (finger) on a sensor or imager or other impression producingstructure), the system may produce an impression (e.g., an image) of aportion of the pattern (fingerprint) of the pattern source (finger) ofthe user. Impressions (images) may be collected and evaluated, in bulkor in realtime or in near realtime depending upon a particularimplementation. Reconstruction of the pattern or of the pattern map mayhappen in a reconstruction space of the system (e.g., a portion ofmemory) or may happen in some other manner, where an initial impression(a foundation image) is selected and placed in an otherwise nullreconstruction space. The system may qualify each impression (image) andmay divide the impressions into a reconstruction set and a reserve setbased upon matching or correlating to the reconstruction. As thequalification proceeds (matching and correlating to the reconstruction)a qualified set of impressions (images) are identified (thereconstruction set) and are added into the reconstruction space as theyare matched/correlated or into the reserve space (e.g., a differentportion of the memory as not matched/not correlated). A reconstruction(e.g., a reconstruction image or other representation of the pattern orpattern map) is produced from the impressions (images) from thereconstruction set and reconstructed in the reconstruction space. Thereconstruction may occur as each impression is added into thereconstruction space or the reconstruction may occur afteridentification of multiple elements of the reconstruction set.Additional reconstructions (i.e., multiplicated reconstructions) may beproduced from a different arrangement of the impressions in thereconstruction set. In some instances it may be sufficient to select anew combination order of the impressions. In other instances, there maybe a selection of impressions based upon how much contribution has beenmade to any particular impression. In other instances, it may be thecase that a new reconstruction includes a limited number of newimpressions from the pattern source with other impressions from thereconstruction set being used to complete any particular multiplicatedreconstruction. In this way, a limited set of impressions in areconstruction set may be used to create an entire set of multiplicatedreconstruction images which collectively represent the same patternsource in slightly different ways. Using this set of multiplicatedreconstructions may improve robustness of a system trying to match tothe pattern source without the inefficiencies and possible useraggravation in providing unique impressions for each reconstruction.

An embodiment of the present invention may use a set of images of apattern for producing a plurality of related, but different,reconstructions of that pattern. When reconstructing the pattern from aset of images of that pattern, each image contributing to thereconstructed pattern has contributed some new information while alsoincluding some duplicated information to allow the image to beappropriately placed. Desirably each image includes just enoughduplicated information to allow it to be placed (within the desiredlevel of confidence) within the pattern while maximizing an amount ofunique information to be added into the reconstruction image. Asexplained further below, an order of processing of the images affectswhich part(s) of which images are duplicated and which parts provideunique information. By intentionally reordering the processing of theimage reconstructions, different but related pattern reconstructions maybe obtained. These different reconstructions may all be registered intoa trusted registration system for example and multiplicate the patternreconstructions without any additional user involvement.

An embodiment may include a machine-implemented method formultiplicating a reconstruction of a portion of a representation of apattern from a set of impressions, each impression including a portionof the pattern, the method including a) identifying a reconstruction setof impressions from the set of impressions that all match or correlateto at least one other impression in the reconstruction set, thereconstruction set of impressions excluding impressions that are do notmatch or do not correlate to at least one other impression in thereconstruction set; b) producing an initial reconstruction from aninitial reconstruction subset of the reconstruction set from a firstcombination of the impressions of the initial reconstruction subset; andc) producing at least one multiplicated reconstruction from the initialreconstruction subset from an additional combination of the impressionsof initial reconstruction subset, each the additional combinationdifferent from all other the combinations.

An embodiment may include an apparatus for multiplicating areconstruction of a portion of a representation of a pattern from a setof impressions, each impression including a portion of the pattern,including a pattern collector producing one or more of the impressions;and a processing system, coupled to the pattern collector, including aprocessor and a memory coupled to the processor, the memory storing aplurality of computer executable instructions wherein the processorexecutes the plurality of computer executable instructions to perform amethod, including a) identifying a reconstruction set of impressionsfrom the set of impressions that all match or correlate to at least oneother impression in the reconstruction set, the reconstruction set ofimpressions excluding impressions that are do not match or do notcorrelate to at least one other impression in the reconstruction set; b)producing an initial reconstruction from an initial reconstructionsubset of the reconstruction set from a first combination of theimpressions of the initial reconstruction subset; and c) producing atleast one multiplicated reconstruction from the initial reconstructionsubset from an additional combination of the impressions of initialreconstruction subset, each the additional combination different fromall other the combinations.

An embodiment may include a non-transitory computer readable medium withcomputer executable instructions stored thereon executed by a processorto perform the method of multiplicating a reconstruction of a portion ofa representation of a pattern from a set of impressions, each impressionincluding a portion of the pattern, the method including a) identifyinga reconstruction set of impressions from the set of impressions that allmatch or correlate to at least one other impression in thereconstruction set, the reconstruction set of impressions excludingimpressions that are do not match or do not correlate to at least oneother impression in the reconstruction set; b) producing an initialreconstruction from an initial reconstruction subset of thereconstruction set from a first combination of the impressions of theinitial reconstruction subset; and c) producing at least onemultiplicated reconstruction from the initial reconstruction subset froman additional combination of the impressions of initial reconstructionsubset, each the additional combination different from all other thecombinations.

An embodiment may include a method for registering a set ofrepresentations of a pattern (for example, 2 or more representations),including a) reconstructing a first representation of the pattern usinga set of impressions associated with each other in a first manner; b)multiplicating the first representation to reconstruct a secondrepresentation of the pattern using the set of impressions associatedwith each other in a second manner different from the first manner; andc) registering the first representation and the second representationinto a trusted memory with both the representations accepted as trustedrepresentations of the pattern. In the method, the pattern may include afingerprint, wherein the first representation includes a firstreconstruction image of the fingerprint, wherein the set of impressionsinclude a set of bitmap images, each of the bitmap images including aportion of the fingerprint, wherein the association between the bitmapimages of the set of bitmap image includes a matching or a correlatingrelationship, wherein the first manner includes a first order ofmatching or correlating the bitmap images of the set of bitmap images,wherein the second representation includes a second reconstruction imageof the fingerprint, and wherein the second manner includes a secondorder of matching or correlating the bitmap images of the set of bitmapimages.

An embodiment may include an apparatus for registering a set ofrepresentations of a pattern (for example two or more representations),including a pattern collector producing one or more of impressions ofthe pattern; and a processing system, coupled to the pattern collector,including a processor and a memory coupled to the processor, the memorystoring a plurality of computer executable instructions wherein theprocessor executes the plurality of computer executable instructions toperform a method, including a) reconstructing a first representation ofthe pattern using a set of impressions associated with each other in afirst manner; b) multiplicating the first representation to reconstructa second representation of the pattern using the set of impressionsassociated with each other in a second manner different from the firstmanner; and c) registering the first representation and the secondrepresentation into a trusted memory with both the representationsaccepted as trusted representations of the pattern.

An embodiment may include a non-transitory computer readable medium withcomputer executable instructions stored thereon executed by a processorto perform the method of registering a set of representations of apattern (for example, two or more), the method including a)reconstructing a first representation of the pattern using a set ofimpressions associated with each other in a first manner; b)multiplicating the first representation to reconstruct a secondrepresentation of the pattern using the set of impressions associatedwith each other in a second manner different from the first manner; andc) registering the first representation and the second representationinto a trusted memory with both the representations accepted as trustedrepresentations of the pattern.

Other embodiments of the present invention may collect a limited numberof new images from additional impressions, the limited number of newimages may be used in conjunction with previously stored images toproduce hybrid pattern reconstruction images. These patternreconstruction images are hybrid in the sense that they include some newimage data from a few new images (few being significantly less thanneeded to reconstruct the pattern) combined with images used in previouspattern reconstructions of the same pattern. In these hybridreconstructions, a priority may be given for use of unique informationfrom the previously used images.

Any of the embodiments described herein may be used alone or togetherwith one another in any combination. Inventions encompassed within thisspecification may also include embodiments that are only partiallymentioned or alluded to or are not mentioned or alluded to at all inthis brief summary or in the abstract. Although various embodiments ofthe invention may have been motivated by various deficiencies with theprior art, which may be discussed or alluded to in one or more places inthe specification, the embodiments of the invention do not necessarilyaddress any of these deficiencies. In other words, different embodimentsof the invention may address different deficiencies that may bediscussed in the specification. Some embodiments may only partiallyaddress some deficiencies or just one deficiency that may be discussedin the specification, and some embodiments may not address any of thesedeficiencies.

Other features, benefits, and advantages of the present invention willbe apparent upon a review of the present disclosure, including thespecification, drawings, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures, in which like reference numerals refer toidentical or functionally-similar elements throughout the separate viewsand which are incorporated in and form a part of the specification,further illustrate the present invention and, together with the detaileddescription of the invention, serve to explain the principles of thepresent invention.

FIG. 1 illustrates a block schematic diagram of an embodiment for apattern registration system;

FIG. 2 illustrates a multiplicating pattern reconstruction process;

FIG. 3 illustrates a first pattern reconstruction state;

FIG. 4 illustrates a second pattern reconstruction state;

FIG. 5 illustrates a third pattern reconstruction state; and

FIG. 6 illustrates a comparison of unique pattern information from eachimage from each of the states of FIG. 3-FIG. 5.

DETAILED DESCRIPTION OF THE INVENTION

Embodiments of the present invention provide a system, method, andcomputer program product for multiplicating pattern images used as atrusted resource while reducing user participation, such as, forexample, a producing N number (N>1) of reconstructed pattern imagesusing shared images common to at least two constructed pattern images.The following description is presented to enable one of ordinary skillin the art to make and use the invention and is provided in the contextof a patent application and its requirements.

Various modifications to the preferred embodiment and the genericprinciples and features described herein will be readily apparent tothose skilled in the art. Thus, the present invention is not intended tobe limited to the embodiment shown but is to be accorded the widestscope consistent with the principles and features described herein.

Definitions

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this general inventive conceptbelongs. It will be further understood that terms, such as those definedin commonly used dictionaries, should be interpreted as having a meaningthat is consistent with their meaning in the context of the relevant artand the present disclosure, and will not be interpreted in an idealizedor overly formal sense unless expressly so defined herein.

The following definitions apply to some of the aspects described withrespect to some embodiments of the invention. These definitions maylikewise be expanded upon herein.

As used herein, the term “or” includes “and/or” and the term “and/or”includes any and all combinations of one or more of the associatedlisted items. Expressions such as “at least one of,” when preceding alist of elements, modify the entire list of elements and do not modifythe individual elements of the list.

As used herein, the singular terms “a,” “an,” and “the” include pluralreferents unless the context clearly dictates otherwise. Thus, forexample, reference to an object can include multiple objects unless thecontext clearly dictates otherwise.

Also, as used in the description herein and throughout the claims thatfollow, the meaning of “in” includes “in” and “on” unless the contextclearly dictates otherwise. It will be understood that when an elementis referred to as being “on” another element, it can be directly on theother element or intervening elements may be present therebetween. Incontrast, when an element is referred to as being “directly on” anotherelement, there are no intervening elements present.

The use of the term “about” applies to all numeric values, whether ornot explicitly indicated. This term generally refers to a range ofnumbers that one of ordinary skill in the art would consider as areasonable amount of deviation to the recited numeric values (i.e.,having the equivalent function or result). For example, this term can beconstrued as including a deviation of ±10 percent of the given numericvalue provided such a deviation does not alter the end function orresult of the value. Therefore, a value of about 1% can be construed tobe a range from 0.9% to 1.1%.

As used herein, the term “set” refers to a collection of one or moreobjects. Thus, for example, a set of objects can include a single objector multiple objects. Objects of a set also can be referred to as membersof the set. Objects of a set can be the same or different. In someinstances, objects of a set can share one or more common properties.

As used herein, the term “adjacent” refers to being near or adjoining.Adjacent objects can be spaced apart from one another or can be inactual or direct contact with one another. In some instances, adjacentobjects can be coupled to one another or can be formed integrally withone another.

As used herein, the terms “connect,” “connected,” and “connecting” referto a direct attachment or link. Connected objects have no or nosubstantial intermediary object or set of objects, as the contextindicates.

As used herein, the terms “couple,” “coupled,” and “coupling” refer toan operational connection or linking. Coupled objects can be directlyconnected to one another or can be indirectly connected to one another,such as via an intermediary set of objects.

As used herein, the terms “substantially” and “substantial” refer to aconsiderable degree or extent. When used in conjunction with an event orcircumstance, the terms can refer to instances in which the event orcircumstance occurs precisely as well as instances in which the event orcircumstance occurs to a close approximation, such as accounting fortypical tolerance levels or variability of the embodiments describedherein.

As used herein, the terms “optional” and “optionally” mean that thesubsequently described event or circumstance may or may not occur andthat the description includes instances where the event or circumstanceoccurs and instances in which it does not.

As used herein, the term “fingerprint” means a map of contrastingamplitude elements from a pattern source. As such, a ridge/furrowpattern on a human finger is included as a fingerprint. Additionally,zebra stripe patterns, retinal vein patterns, or other collections ofcontrasting amplitude elements having a set of a plurality ofsufficiently long succession of similarly contrasted elements.

As used herein, the terms “match,” “matching,” and “matches” refer to aconclusion of a comparison of a first pattern (e.g., a first image of afirst impression from a sensor) against a second pattern (e.g., a secondimage of a second impression from the sensor) that means that a patternsource used in the first impression is the same pattern source, within asufficient confidence interval appropriate to the application, used inthe second impression. A match does not require 100% commonality offeatures between the first and second patterns. One hundred percent, andnearly 100% (for example 95% commonality—depending upon application),commonality is referred to as a duplicate image. This results when theuser places the same portion of the same finger on the sensor from twoor more impressions.

As used herein, the terms “correlate,” “correlating,” and “correlated”refer to a conclusion of a comparison of a first pattern (e.g., a firstimage of a first impression from a sensor) against a second pattern(e.g., a second image of a second impression from the sensor) that meansthat a pattern source used in the first impression is the same patternsource, within a sufficient confidence interval appropriate to theapplication, used in the second impression without a match between thefirst pattern and the second pattern. Correlation is found by matchingintermediate patterns that provide a bridge between the first patternand the second pattern. For example, a second pattern may not match thefirst pattern, but may match a third pattern, with the third patternmatching the first pattern. Correlation may be found by one or moreintermediate matches between the second pattern and the first pattern.

As used herein, the terms “multiplicate,” “multiplicating,” and“multiplicated” refer to a production of one or more additionalstructures, representations, patterns, reconstructions, reconstructionimages, or the like that are, at a superficial or macro level, aduplication of a particular original structure, representation, pattern,reconstructions, reconstruction images, or the like but include, uponcloser examination, differences that may be recognized andevaluated/considered by a process (such as a matching or alignmentevaluation engine) that evaluates the original and additionalstructures, representations, patterns, reconstructions, reconstructionimages against a structure under-test. At one level of consideration,they are duplicates of each other as they each are representative of thesame pattern but at another level of consideration they are alldifferent as they will include variations not shared among all themultiplicated structures.

Generally, the basic idea is that reconstruction of a pattern from a setof images of that pattern rely on partially unique data and partlyduplicated data. A ratio of unique data to duplicated data typicallyvaries for each image. In a particular implementation, the amount ofduplicated data needs to be sufficient to enable the image to beaccurately positioned and is desirably reduced as much as possible whileretaining a desired level of confidence of the accurate positioning. Theunique information is used to increase the pattern reconstruction. Whenreconstructing a pattern, an order in which the images are useddetermines which parts of an image are unique and which parts areduplicated. By multiplicating the pattern reconstruction using differentunique contributions from the set of images, multiple distinct, butrelated, pattern reconstructions can be generated. It may be the casethat a matching engine would not match each pattern construction withall the others, but each pattern reconstruction is expected to match atleast one other multiplicated pattern reconstruction. In this way, anytest image derived from the same pattern has an increased chance ofmatching at least one of the multiplicated pattern reconstructions. Achance of success improves as the number of multiplicated patternreconstructions increases.

In other embodiments, a quality of the set of multiplicated patternreconstructions may be improved by receiving a limited number of newimpressions, and new images, for each multiplicated patternreconstruction. Depending upon what portion of the pattern is providedby the new image(s), unique data from the previously used images isprioritized in creating a new pattern reconstruction. Each new imagereconstruction thus greatly increases a quality of the set ofmultiplicated pattern reconstructions, such as for registration, toimprove subsequent uses.

The herein described methods can be implemented in the general contextof instructions executed by a stored program computer system coupled toa memory storing those instructions. Such computer-executableinstructions may include programs, routines, objects, components, datastructures, and computer software technologies that can be used toperform particular tasks and process abstract data types. Softwareimplementations of the above described methods may be coded in differentlanguages for application in a variety of computing platforms andenvironments. It will be appreciated that the scope and underlyingprinciples of the above described methods are not limited to anyparticular computer software technology.

Moreover, those skilled in the art will appreciate that the hereindescribed methods may be practiced using any one or a combination ofcomputer processing system configurations, including, but not limitedto, single and multi-processer systems, hand-held devices, programmableconsumer electronics, mini-computers, or mainframe computers. The abovedescribed methods may also be practiced in distributed computingenvironments where tasks are performed by servers or other processingdevices that are linked through a one or more data communicationsnetworks. In a distributed computing environment, program modules may belocated in both local and remote computer storage media including memorystorage devices.

Also, an article of manufacture for use with a computer processor, suchas a CD, pre-recorded disk or other equivalent devices, could include acomputer program storage medium and program means recorded thereon fordirecting the computer processor to facilitate the implementation andpractice of the above described methods. Such devices and articles ofmanufacture also fall within the spirit and scope of the presentinvention.

As will be described, the invention can be implemented in numerous ways,including for example as a method (including a computer-implementedmethod), a system (including a computer processing system), anapparatus, a computer readable medium, a computer program product, agraphical user interface, a web portal, or a data structure tangiblyfixed in a computer readable memory. Several embodiments of the presentinvention are discussed below. The appended drawings illustrate onlytypical embodiments of the present invention and therefore are not to beconsidered limiting of its scope and breadth.

FIG. 1 illustrates a block schematic diagram of an embodiment for apattern registration system 100. System 100 includes an imaging device105, a processor 110, an input/output (I/O) system 115, a nonvolatilememory 120 and a RAM memory 125, with memory 120 and memory 125collectively defining a memory system 130. System 100 is described, inthe disclosed embodiment, as a fingerprint registration system that maybe used as a pattern (e.g., fingerprint) verification system. In afingerprint verification system, the system attempts to measure acorrespondence between a pair of fingerprints (one-on-one) in order toestablish, within some level of confidence, whether one pattern sourceof one fingerprint is the same or sufficiently close as a pattern sourceof the other fingerprint. This is contrasted with an identificationsystem that determines which pattern source generated a particularfingerprint. A verification system may be used as an identificationsystem when a decrease in power/speed is acceptable, given fixedresources. A verification system performs better as the quality of theregistered images improves.

System 100 may function as a basic computer in implementing the presentinvention for accessing and processing fingerprints, fingerprint images,and sets of curves derived from a fingerprint as further describedbelow. Processor 110 may include one or more central processing units(CPUs), selected from one or more of an x86, x64, ARM, or the like,architectures, connected to various other components, such as by asystem bus.

Imaging device 105 produces an image of a fingerprint; either directly(e.g., it is a sensor or imager for a pattern source or an artifact froma pattern source) or it accesses a data structure or memory to obtainthe image. The image may be of all or a portion of an entirefingerprint. Sometimes a portion of a fingerprint image may appear to bea set of discrete curves. System 100 is a computing system (e.g., anembedded computing system, a general purpose computing system, a specialpurpose computing system, combinations thereof, including stored programcomputing platform with a processor and a coupled memory storingexecutable instructions) having a large number of suitableimplementations for accessing and processing resources fingerprints,fingerprint images, portions of fingerprint images, and sets of curvesderived from a fingerprint. Sensors that may be used with system 100include charge-coupled devices (CCD), complementary metal oxidesemiconductor (CMOS), capacitive, thermal, optical, electro-optical, RFmodulation, acoustic, or other image sensing devices, such as thoseavailable from a wide range of manufacturers including IDEX ASA,Fujitsu, Atmel, Apple, Synaptics, Infineon, Sony, Integrated Biometrics,and Fingerprint Cards for example. Image arrays may be relatively small(e.g., 50×50 pixels, 128×128 pixels to a CIF size of 352×288 pixels orlarger), each pixel having a pixel depth of but not limited to eightbits. System 100 uses a fingerprint image produced from device 105. Insome cases, device 105 may preprocess images, such as performing imagekeystone corrections (a geometric correction used to account for opticaldistortions associated with optical/prism based systems when returningan image size proportionate to fingerprint size or image reconstructionto assemble an image taken in bands as a finger is ‘swiped’ across thesensor.

An operating system runs on processor 110, providing control andcoordinating the functions of the various components of the system. Theoperating system may be one of the commercially available operatingsystems such as Microsoft (e.g., windows), Apple (e.g., IOS or Mac OSX), Google (e.g., Chrome or Android), as well as UNIX and AIX operatingsystems, though some embodiments may use a custom control for providingminimal, tailored functions. Custom programs, controlled by the system,include sets of instructions executable on processor 110 that are movedinto and out of memory. These sets of instructions, when executed byprocessor 110, perform the methods and automated machine-implementedprocesses described herein. Device 105, I/P communication system 115,and memory system 130 are each coupled to processor 110 via a bus andwith memory system 130 including a Basic Input/Output System (BIOS) forcontrolling the basic system functions.

I/O system 115 interconnects system 100 with outside devices ornetworks, enabling the system to communicate with other such systemsover a communications system (e.g., directly wired, Local Area Network(LAN) or Wide Area Network (WAN), which includes, for example, theInternet, the WEB, intranets, extranets, and other public and privatenetworks, wired, optical, or wireless). The terms associated with thecommunications system are meant to be generally interchangeable and areso used in the present description of the distribution network. I/Odevices are also connected to the system bus via I/O system 115. Akeyboard, a pointing device (e.g., mouse, trackball or other device) anda display or indicator may be interconnected to system 100 through I/Osystem 115. It is through such input devices that the user mayinteractively relate to the programs for manipulating the resources,images, subsystems, processes and system according to the presentinvention. By using the aforementioned I/O devices, a user is capable ofinputting information to the system through the keyboard or mouse andreceiving output information from the system. The system may contain aremovable memory component for transferring data, for example images,maps, instructions, or programs.

In use, system 100 processes a set of pattern images from a patternsource (e.g., a fingerprint) to reconstruct a plurality of images of thepattern source. In some embodiments, each reconstructed image includes afew new images collected specifically for a particular reconstruction,with unused unique portions of previously used (in a different patternreconstruction for example) images given priority over used portions ofpreviously used images. Duplicated information in stored images may alsobe used on a lower priority as needed to develop any multiplicatedreconstruction to a desired quality metric.

FIG. 2 illustrates a multiplicating pattern reconstruction process 200including steps 205-225. Process 200 begins with step 205 and starts themethod, including any variable initialization and resource reservation.After step 205, process 200 includes a reconstruction of a first patternreconstruction. There are many ways that this may be accomplished. Forexample, U.S. Patent Application No. 62/185,004 and U.S. patentapplication Ser. No. 15/192,099, both titled Pattern Mapping and U.S.Patent Application No. 62/189,488 and U.S. patent application Ser. No.15/201,901 both titled Image Reconstruction both include examples ofpattern reconstruction processes that may be included in an acceptablereconstruction process, among other possible reconstruction methods,included with step 210 (the contents of these applications are herebyexpressly incorporated by reference thereto in their entireties for allpurposes).

In reconstruction step 210, multiple impressions are sampled from apattern, each impression producing an image of the pattern. Asreferenced in the incorporated patent applications, some implementationsof pattern mapping or pattern reconstruction may desirably remove imagesused in a mapped or reconstructed pattern. In contrast, the illustratedembodiments provide for storage of images of a source pattern. Someimplementations may prefer that an image is removed from considerationin new image constructions when a particular amount of an image has beenused, when an image has been used a particular number of times, or othermetric. Reconstruction step 210 may, in some embodiments, flag duplicateimages or redundant data in a matching image for later use in process200.

After step 210, process 200 tests a number of multiplicated patternreconstructions at step 215 to determine whether a desired number ofpattern reconstructions have been produced. This number may beprespecified or it may be dynamically established based upon somequality metric of the set of multiplicated pattern reconstructions. Oncesuch quality metric may indicate how much duplicated information is usedin the set of multiplicated pattern reconstructions. Some applicationsmay desire to place an upper threshold on how much duplicated data isreused.

When the test at step 215 is FALSE and the desired number of patternreconstructions have not been produced, process 200 performs step 220 toproduce an addition pattern reconstruction. Step 220 may, in someinstances, process the images used in an existing pattern reconstructionin a different order than all other previous orders. As illustrated inFIG. 3-6, altering a processing order of images produces a uniquepattern reconstruction as compared to pattern reconstructions using adifferent processing order.

In other embodiments, step 220 may collect new images from the user. Insuch a case, step 220 is different from step 215 in that process 200does not collect a sufficient number of new images to completelyreconstruct a pattern. Rather, a new foundation image is collected andreconstruction then begins reconstructing the pattern based upon the newfoundation image. Based upon the new foundation image, images storedfrom previous reconstruction steps are used to reconstruct the image.After each addition to the pattern reconstruction, a priority may begiven to test for a match against the currently stored images. When amatch is established, the pattern reconstruction may blend the matchingimage in the pattern reconstruction. Then the new pattern reconstructionis again tested against the stored images. The processing continuinguntil the reconstructed image has sufficient quality or a new imageneeds to be collected from the user. During processing, in the eventthat multiple images match the pattern reconstruction, step 220 may givea priority to previously unused images over previously used images. Inthe event that multiple previously used images match the patternreconstruction, then step 220 may give a priority to that previouslyused image having a greater portion of previously unused area. Otherconditions may be used to establish a new processing order, select whichstored image to use in the new processing order, and determining whenand if process 200 asks the user for a new impression to collect a newimage.

After step 220, process 200 returns to the test at step 215 anddetermines whether the set of multiplicated pattern reconstructions iscomplete. Process 200 continues to cycle through step 220 and step 215until the test at step 215 is TRUE and the set of multiplicated patternreconstructions is complete. When complete, process 200 advances to step225 and terminates the multiplication of pattern reconstructions usingprocess 200. Some output or post-reconstruction processing may occur atstep 225.

FIG. 3-FIG. 6 illustrate multiplicated pattern reconstructions from asingle set of images, without addition of new images. FIG. 3 illustratesa first pattern reconstruction state 300 from a set 305 of images usedto create a first pattern reconstruction 310. Set 305 includes N number(in this example N=5) of images 315. State 300 also includes anunderstanding of which portions of a used image 320 have been used andwhich portions have not been used. In images 320 _(i), corresponding toimages 315 _(i) used in pattern reconstruction 310, the masked (void)areas in used image 320 _(i)indicate duplicated information from aparticular image 315 _(i). To simplify the discussion, patternreconstruction 310 has used all N number of images 315. An order ofprocessing images 315 _(i) to produce pattern reconstruction 310 couldbe, i=1, 2, 3, 4, 5. It is seen that 315 _(i,i=1) is the foundationimage as 100% of used image 320 ₁ is used in pattern reconstruction 310.Thereafter, different areas of each image 315 _(i,i=2-5) are processed(e.g., translated and rotated) to align the pattern information of eachparticular image 315 with pattern reconstruction 310. Process 200 maymark each used image 320 to indicate how much pattern information of theparticular image was duplicated and how much was unique.

FIG. 4 illustrates a second reconstruction state 400 from set 305 ofimages used to create a second pattern reconstruction 410, differentfrom, but matchable to (within some degree of confidence) first patternreconstruction 310 of FIG. 3. State 400 also includes an understandingof which portions of a used image 420 have been used and which portionshave not been used in second pattern reconstruction 410. In images 420_(i), corresponding to images 315 _(i) used in pattern reconstruction410, the masked (void) areas in used image 420 _(i) indicate duplicatedinformation from a particular image 315 _(i). An order of processingimages 315 _(i) to produce second pattern reconstruction 410 could be,i=3, 2, 1, 4, 5. It is seen that 315 _(i,i=3) is the foundation image as100% of used image 420 ₃ is used in second pattern reconstruction 410.Thereafter, different areas of each image 315 _(i), are processed (e.g.,translated and rotated) to align the pattern information of eachparticular image 315 with second pattern reconstruction 410 as it isbeing reconstructed. Process 200 may mark each used image 420 toindicate how much pattern information of the particular image wasduplicated and how much was unique.

A comparison of used images 420 _(i) with counterpart used images 320_(i) demonstrates that second pattern reconstruction 410, while being areconstruction of the same pattern, has included different patterninformation as compared to first pattern reconstruction 310.

FIG. 5 illustrates a third reconstruction state 500 from set 305 ofimages used to create a third pattern reconstruction 510, differentfrom, but matchable to (within some degree of confidence) first patternreconstruction 310 of FIG. 3 and second pattern reconstruction 410 ofFIG. 4. State 500 also includes an understanding of which portions of aused image 420 have been used and which portions have not been used inthird pattern reconstruction 510. In used images 520 _(i), correspondingto images 315 _(i) usedin third pattern reconstruction 510, the masked(void) areas in used image 520 _(i) indicate duplicated information froma particular image 315 _(i). An order of processing images 315 _(i) toproduce third pattern reconstruction 510 could be, i=5, 4, 3, 2, 1. Itis seen that 315 _(i,i=5) is the foundation image as 100% of used image520 ₅ is used in third pattern reconstruction 510. Thereafter, differentareas of each image 315 _(i), are processed (e.g., translated androtated) to align the pattern information of each particular image 315with third pattern reconstruction 510 as it is being reconstructed.Process 200 may mark each used image 520 to indicate how much patterninformation of the particular image was duplicated and how much wasunique.

A comparison of used images 520 _(i) with counterpart used images 320_(i) and used images 410 _(i) demonstrates that third patternreconstruction 510, while being a reconstruction of the same pattern,has included different pattern information as compared to both firstpattern reconstruction 310 and second pattern reconstruction 410.

FIG. 6 illustrates a comparison 600 of corresponding used images 320_(i), 420 _(i), and 520 _(i) from images 315 _(i) of set 305. Asdescribed herein, each set of used images indicate which patterninformation was used in a corresponding pattern reconstruction.Comparison 600 illustrates how each pattern reconstruction will be ofthe same pattern, but include different pattern information from anyother pattern reconstruction. Ideally, without noise, the patternreconstructions in FIG. 3-FIG. 5 would be exact matches. However, noisecontributions are random and will cause differences, many of which aresmall. On the scale of match testing, some small errors, especially inan event when they accumulate, may result in very similar, butsignificantly different computationally, pattern reconstructions. Thisis especially true in the case that the pattern reconstruction methodsintroduce noise. This can be the case with certain rotation and imagemanipulations. This is one reason why a different order results indifferent images being rotated differently. The ordering thus introducesdifferent noise contributions to the used images than is the case withthe same image used in a different pattern reconstruction of the samepattern.

Moreover, those skilled in the art will appreciate that the abovedescribed methods may be practiced using any one or a combination ofcomputer processing system configurations, including, but not limitedto, single and multi-processer systems, hand-held devices, programmableconsumer electronics, mini-computers, or mainframe computers. The abovedescribed methods may also be practiced in distributed computingenvironments where tasks are performed by servers or other processingdevices that are linked through a one or more data communicationsnetworks. In a distributed computing environment, program modules may belocated in both local and remote computer storage media including memorystorage devices.

Also, an article of manufacture for use with a computer processor, suchas a CD, pre-recorded disk or other equivalent devices, could include acomputer program storage medium and program mechanisms recorded thereonfor directing the computer processor to facilitate the implementationand practice of the above described methods. Such devices and articlesof manufacture also fall within the spirit and scope of the presentinvention.

The invention can be implemented in numerous ways, including for exampleas a method (including a computer-implemented method), a system(including a computer processing system, general purpose, specialpurpose, hybrid, embedded, and the like), an apparatus, a computerreadable medium, a computer program product, a graphical user interface,a web portal, or a data structure tangibly fixed in a computer readablememory. Several embodiments of the present invention are discussedherein. The appended drawings illustrate only typical embodiments of thepresent invention and therefore are not to be considered limiting of itsscope and breadth. The system, methods, and computer-program productshave been described in general terms as an aid to understanding detailsof preferred embodiments of the present invention. In the descriptionherein, numerous specific details are provided, such as examples ofcomponents and/or methods, to provide a thorough understanding ofembodiments of the present invention. Some features and benefits of thepresent invention are realized in such modes and are not required inevery case. One skilled in the relevant art will recognize, however,that an embodiment of the invention can be practiced without one or moreof the specific details, or with other apparatus, systems, assemblies,methods, components, materials, parts, and/or the like. In otherinstances, well-known structures, materials, or operations are notspecifically shown or described in detail to avoid obscuring aspects ofembodiments of the present invention.

System 100 includes a computer program product or software that isstored on or in a non-transitory processor readable medium. Currentexamples of a processor readable medium include, but are not limited to,an electronic circuit, a semiconductor memory device, a ROM, a flashmemory, an erasable programmable ROM (EPROM), a floppy diskette, acompact disk (CD-ROM), an optical disk, a hard disk, and a fiber opticmedium. As will be described more fully herein, the software can includea plurality of modules for performing system tasks such as performingthe methods previously described herein. A processor interpretsinstructions to execute the software, as well as, generates automaticinstructions to execute software for system responsive to predeterminedconditions. Instructions from both the user interface and the softwareare processed by the processor for operation of system 100. In someembodiments, a plurality of processors can be utilized such that systemoperations can be executed more rapidly.

The system and methods above has been described in general terms as anaid to understanding details of preferred embodiments of the presentinvention. In the description herein, numerous specific details areprovided, such as examples of components and/or methods, to provide athorough understanding of embodiments of the present invention. Somefeatures and benefits of the present invention are realized in suchmodes and are not required in every case. One skilled in the relevantart will recognize, however, that an embodiment of the invention can bepracticed without one or more of the specific details, or with otherapparatus, systems, assemblies, methods, components, materials, parts,and/or the like. In other instances, well-known structures, materials,or operations are not specifically shown or described in detail to avoidobscuring aspects of embodiments of the present invention.

Reference throughout this specification to “one embodiment”, “anembodiment”, or “a specific embodiment” means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the present invention and notnecessarily in all embodiments. Thus, respective appearances of thephrases “in one embodiment”, “in an embodiment”, or “in a specificembodiment” in various places throughout this specification are notnecessarily referring to the same embodiment. Furthermore, theparticular features, structures, or characteristics of any specificembodiment of the present invention may be combined in any suitablemanner with one or more other embodiments. It is to be understood thatother variations and modifications of the embodiments of the presentinvention described and illustrated herein are possible in light of theteachings herein and are to be considered as part of the spirit andscope of the present invention.

It will also be appreciated that one or more of the elements depicted inthe drawings/figures can also be implemented in a more separated orintegrated manner, or even removed or rendered as inoperable in certaincases, as is useful in accordance with a particular application.

Additionally, any signal arrows in the drawings/Figures should beconsidered only as exemplary, and not limiting, unless otherwisespecifically noted. Combinations of components or steps will also beconsidered as being noted, where terminology is foreseen as renderingthe ability to separate or combine is unclear.

The foregoing description of illustrated embodiments of the presentinvention, including what is described in the Abstract, is not intendedto be exhaustive or to limit the invention to the precise formsdisclosed herein. While specific embodiments of, and examples for, theinvention are described herein for illustrative purposes only, variousequivalent modifications are possible within the spirit and scope of thepresent invention, as those skilled in the relevant art will recognizeand appreciate. As indicated, these modifications may be made to thepresent invention in light of the foregoing description of illustratedembodiments of the present invention and are to be included within thespirit and scope of the present invention.

Thus, while the present invention has been described herein withreference to particular embodiments thereof, a latitude of modification,various changes and substitutions are intended in the foregoingdisclosures, and it will be appreciated that in some instances somefeatures of embodiments of the invention will be employed without acorresponding use of other features without departing from the scope andspirit of the invention as set forth. Therefore, many modifications maybe made to adapt a particular situation or material to the essentialscope and spirit of the present invention. It is intended that theinvention not be limited to the particular terms used in followingclaims and/or to the particular embodiment disclosed as the best modecontemplated for carrying out this invention, but that the inventionwill include any and all embodiments and equivalents falling within thescope of the appended claims. Thus, the scope of the invention is to bedetermined solely by the appended claims.

What is claimed as new and desired to be protected by Letters Patent ofthe United States is:
 1. A machine-implemented method for multiplicatinga reconstruction of a portion of a representation of a pattern from aset of impressions, each impression including a portion of the pattern,the method comprising: a) identifying a reconstruction set ofimpressions from the set of impressions that all match or correlate toat least one other impression in said reconstruction set, saidreconstruction set of impressions excluding impressions that are do notmatch or do not correlate to at least one other impression in saidreconstruction set; b) producing an initial reconstruction from aninitial reconstruction subset of said reconstruction set from a firstcombination of said impressions of said initial reconstruction subset;and c) producing at least one multiplicated reconstruction from saidinitial reconstruction subset from an additional combination of saidimpressions of initial reconstruction subset, each said additionalcombination different from all other said combinations.
 2. The method ofclaim 1 wherein each impression of said initial reconstruction subsetcontributes a unique pattern portion to its associated reconstructionand wherein each said combination includes a unique arrangement ofdiffering unique pattern portions from said initial reconstruction set.3. The method of claim 1 wherein said step b) comprises: b1) selectingone of the impressions as a first foundation impression having a firstfoundation pattern portion of a first foundation pattern source; b2)processing the set of impressions to determine a reconstruction set ofimpressions, the portion of the pattern of each said impression of saidreconstruction set matched or correlated to said first foundationpattern portion, wherein said reconstruction set of impressions excludeseach impression from the set of impressions with its portion of thepattern not matched or not correlated to said first foundation patternportion; and b3) combining said impressions of said reconstruction setinto said initial reconstruction, said initial reconstruction includingthe portion of the representation spanning a reconstruction metricgreater than an impression metric of any single impression of saidreconstruction set, wherein said initial reconstruction subset includesimpressions from said reconstruction set contributing to said initialreconstruction, and wherein said first combination includes a firstparticular order of combining said impressions of said initialreconstruction subset to produce said initial reconstruction.
 4. Themethod of claim 3 wherein said step c) comprises: c1) selecting anotherone of the impressions different from the impression used for said firstfoundation impression as a second foundation impression having a secondfoundation pattern portion of said first foundation pattern source; andc2) combining said impressions of said reconstruction set into one ofsaid additional reconstructions, each particular said additionalreconstruction including the portion of the representation spanning areconstruction metric greater than an impression metric of any singleimpression of said reconstruction set, wherein said initialreconstruction subset includes impressions from said reconstruction setcontributing to said particular additional reconstruction, and whereineach said additional combination includes an additional particular orderof combining said impressions of said initial reconstruction subset toproduce said differing multiplicated additional reconstructions.
 5. Themethod of claim 2 wherein said step b) comprises: b1) selecting one ofthe impressions as a first foundation impression having a firstfoundation pattern portion of a first foundation pattern source; b2)processing the set of impressions to determine a reconstruction set ofimpressions, the portion of the pattern of each said impression of saidreconstruction set matched or correlated to said first foundationpattern portion, wherein said reconstruction set of impressions excludeseach impression from the set of impressions with its portion of thepattern not matched or not correlated to said first foundation patternportion; and b3) combining said impressions of said reconstruction setinto said initial reconstruction, said initial reconstruction includingthe portion of the representation spanning a reconstruction metricgreater than an impression metric of any single impression of saidreconstruction set, wherein said initial reconstruction subset includesimpressions from said reconstruction set contributing to said initialreconstruction, and wherein said first combination includes a firstparticular order of combining said impressions of said initialreconstruction subset to produce said initial reconstruction.
 6. Themethod of claim 5 wherein said step c) comprises: c1) selecting anotherone of the impressions different from the impression used for said firstfoundation impression as a second foundation impression having a secondfoundation pattern portion of said first foundation pattern source; andc2) combining said impressions of said reconstruction set into one ofsaid additional reconstructions, each particular said additionalreconstruction including the portion of the representation spanning areconstruction metric greater than an impression metric of any singleimpression of said reconstruction set, wherein said initialreconstruction subset includes impressions from said reconstruction setcontributing to said particular additional reconstruction, and whereineach said additional combination includes an additional particular orderof combining said impressions of said initial reconstruction subset toproduce said differing multiplicated additional reconstructions; whereineach said additional particular order selects any particular oneimpression from said initial reconstruction subset responsive to aninverse weighting of a ratio of said unique pattern portion to a totalpattern portion used in any previous said combination.
 7. The method ofclaim 1 wherein each said additional reconstruction includes at leastone impression not shared by all other said reconstructions.
 8. Themethod of claim 1 wherein each said additional reconstruction includesat least one impression not shared by any other said reconstruction. 9.The method of claim 4 wherein each said additional reconstructionincludes a unique impression not shared by any other saidreconstruction, said unique impression included as said secondfoundation impression.
 10. The method of claim 1 further comprising: d)registering said reconstructions into a trusted memory with all saidreconstructions accepted as trusted representations of the pattern. 11.An apparatus for multiplicating a reconstruction of a portion of arepresentation of a pattern from a set of impressions, each impressionincluding a portion of the pattern, comprising: a pattern collectorproducing one or more of the impressions; and a processing system,coupled to said pattern collector, including a processor and a memorycoupled to said processor, said memory storing a plurality of computerexecutable instructions wherein said processor executes said pluralityof computer executable instructions to perform a method, comprising: a)identifying a reconstruction set of impressions from the set ofimpressions that all match or correlate to at least one other impressionin said reconstruction set, said reconstruction set of impressionsexcluding impressions that are do not match or do not correlate to atleast one other impression in said reconstruction set; b) producing aninitial reconstruction from an initial reconstruction subset of saidreconstruction set from a first combination of said impressions of saidinitial reconstruction subset; and c) producing at least onemultiplicated reconstruction from said initial reconstruction subsetfrom an additional combination of said impressions of initialreconstruction subset, each said additional combination different fromall other said combinations.
 12. A non-transitory computer readablemedium with computer executable instructions stored thereon executed bya processor to perform the method of multiplicating a reconstruction ofa portion of a representation of a pattern from a set of impressions,each impression including a portion of the pattern, the methodcomprising: a) identifying a reconstruction set of impressions from theset of impressions that all match or correlate to at least one otherimpression in said reconstruction set, said reconstruction set ofimpressions excluding impressions that are do not match or do notcorrelate to at least one other impression in said reconstruction set;b) producing an initial reconstruction from an initial reconstructionsubset of said reconstruction set from a first combination of saidimpressions of said initial reconstruction subset; and c) producing atleast one multiplicated reconstruction from said initial reconstructionsubset from an additional combination of said impressions of initialreconstruction subset, each said additional combination different fromall other said combinations.
 13. A method for registering a set ofrepresentations of a pattern, comprising: a) reconstructing a firstrepresentation of the pattern using a set of impressions associated witheach other in a first manner; b) multiplicating said firstrepresentation to reconstruct a second representation of the patternusing said set of impressions associated with each other in a secondmanner different from said first manner; and c) registering said firstrepresentation and said second representation into a trusted memory withboth said representations accepted as trusted representations of thepattern.
 14. The method of claim 13 wherein the pattern includes afingerprint, wherein said first representation includes a firstreconstruction image of the fingerprint, wherein said set of impressionsinclude a set of bitmap images, each of said bitmap images including aportion of said fingerprint, wherein said association between saidbitmap images of said set of bitmap image includes a matching or acorrelating relationship, wherein said first manner includes a firstorder of matching or correlating said bitmap images of said set ofbitmap images, wherein said second representation includes a secondreconstruction image of the fingerprint, and wherein said second mannerincludes a second order of matching or correlating said bitmap images ofsaid set of bitmap images.
 15. An apparatus for registering a set ofrepresentations of a pattern, comprising: a pattern collector producingone or more of impressions of the pattern; and a processing system,coupled to said pattern collector, including a processor and a memorycoupled to said processor, said memory storing a plurality of computerexecutable instructions wherein said processor executes said pluralityof computer executable instructions to perform a method, comprising: a)reconstructing a first representation of the pattern using a set ofimpressions associated with each other in a first manner; b)multiplicating said first representation to reconstruct a secondrepresentation of the pattern using said set of impressions associatedwith each other in a second manner different from said first manner; andc) registering said first representation and said second representationinto a trusted memory with both said representations accepted as trustedrepresentations of the pattern.
 16. A non-transitory computer readablemedium with computer executable instructions stored thereon executed bya processor to perform the method of registering a set ofrepresentations of a pattern, the method comprising: a) reconstructing afirst representation of the pattern using a set of impressionsassociated with each other in a first manner; b) multiplicating saidfirst representation to reconstruct a second representation of thepattern using said set of impressions associated with each other in asecond manner different from said first manner; and c) registering saidfirst representation and said second representation into a trustedmemory with both said representations accepted as trustedrepresentations of the pattern.